Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Segmentation Techniques

Abstract

ABSTRACT A multi-stage approach for plant leaf disease classification using Convolutional Neural Networks (CNN) and image segmentation is presented. The system identifies plant types and classifies diseases from leaf images. Preprocessing includes noise removal and image enhancement, followed by K-means clustering for segmentation. A hybrid CNN model performs accurate classification of plant species and disease type, and suitable fertilizer recommendations are provided. The method achieves accuracies of 99.8%, 96.5%, and 98.3% on apple, tomato, and grape datasets. The results show improved accuracy and robustness, making the system effective for practical agricultural applications. INDEX TERMS Convolutional Neural Network (CNN), Image Segmentation, K-means clustering, Plant Leaf Diseases.

Plant phenotyping relevance

葉画像から病害状態を直接推定するCNN・画像セグメンテーション手法が研究の中心であり、植物病害フェノタイピング手法として該当する。

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